Towards a Responsible AI Adoption/Adaptation (RAA) Ecosystem: Vision and Model to Keep Socio-Technological Balance

Abstract

Abstract The rapid adoption of AI technologies is outpacing our ability to assess their long-term societal and economic impacts. Initially, AI was expected to automate only repetitive, low-skill tasks. However, presently, highly skilled roles such as in software development, manufacturing, and finance are being automated. Tasks that once required many professionals can now be managed by a few, disproportionately benefiting those with greater resources and economic power, such as large corporations. This trend may lead to a growing socio-technological imbalance, for instance, a growing mismatch between the rapid advancements in artificial intelligence and society’s ability to adapt to and govern these changes in a fair, ethical, and inclusive manner. As AI-driven automation is increasingly being adopted across almost all business domains, covering all process-level functions. Additionally, there is a lack of practical methods for the ethical and responsible adoption of AI, which are either not being implemented or not well understood by stakeholders in the business ecosystem. This emphasizes the role of Responsible AI (RAI), which is becoming critical and essential in addressing the socio-technological imbalance in the organizational context regarding adopting/adapting AI. RAI ensures that systems are developed and deployed with core principles such as explainability, fairness, ethics, transparency, accountability, human oversight, and privacy. Furthermore, AI itself presents as a technological capability to promote responsible behavior by assessing, predicting, supporting, and regulating its own societal (i.e., organization-specific) and technological impacts. This study explores this dual perspective by reviewing the literature to propose a RAI Adoption/Adaptation (RAA) framework, focusing on the business process as the primary unit of AI intervention. A conceptual system implementation context is proposed to illustrate RAA enablement at the process level and highlight the need for an RAI agentic architecture capable of measuring, analyzing, and forecasting AI’s impact on business processes to support ethical, balanced innovation.

Ähnliche Arbeiten